期刊
TECHNOMETRICS
卷 45, 期 1, 页码 1-15出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/004017002188618626
关键词
classification analysis; discriminant analysis; medical diagnosis; multivariate analysis; pattern recognition; signal-to-noise ratio; Taguchi methods
The Mahalanobis-Taguchi system (MTS) is a relatively new collection of methods proposed for diagnosis and forecasting using mutlivariate data. The primary proponent of the NITS is Genichi Taguchi, who is very well known for his controversial ideas and methods for using designed experiments. The NITS results in a Mahalanobis distance scale used to measure the level of abnormality of abnormal items compared to a group of normal items. First, it must be demonstrated that a Mahalanobis distance measure based on all available variables on the items is able to separate the abnormal items from the normal items. If this is the case, then orthogonal arrays and signal-to-noise ratios are used to select an optimal combination of variables for calculating the Mahalanobis distances. Optimality is defined in terms of the ability of the Mahalanobis distance scale to match a prespecified or estimated scale that measures the severity of the abnormalities. In this expository article. we review the methods of the NITS and use a case study based on medical data to illustrate them. We identify some conceptual, operational, and technical issues with the NITS that lead us to advise against its use.
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